机器学习预测使用临床参数对结核病患者药剂师干预的益处:单中心回顾性研究
Tingting Li1, Huanqing Liu2, Zhuhong You3
1Drug Clinical Trial Institution Office, Xi'an Chest Hospital, Xi'an, Shaanxi, China.
Frontiers in cellular and infection microbiology
|February 20, 2026
概括
机器学习可以准确地预测哪些结核病患者可以从药剂师的干预中受益,从而改善治疗资源的分配. 这种模式增强了结核病患者的个性化护理.
科学领域:
- * 机器学习在医疗保健中的应用.
- * 临床信息学和预测建模
背景情况:
- *结核病 (TB) 构成了全球健康负担,需要有效的患者管理.
- * 确定需要药剂师干预的患者对于优化结果和资源配置至关重要.
- *使用临床参数的预测建模可以帮助药剂师针对结核病患者进行有针对性的干预.
研究的目的:
- * 开发和验证一种机器学习模型,用于预测药剂师在结核病患者入院时的干预组分配.
- * 通过先进的功能工程和超参数调整来优化模型.
- *通过转录基因数据分析来探索生物学见解,以获得上下文理解.
主要方法:
- *对467名结核病患者的回顾性分析.
- * 数据增强技术 (多邻域插值) 用于将数据集大小增加到1,999个样本.
- * 广泛的特征工程 (122个特征) 和基于Optuna的超参数优化,用于多级合集模型 (43个基本模型).
主要成果:
- *整体模型在预测药剂师干预组分配时实现了92.25%的准确性和96.96%的AUC-ROC.
- *对GEO数据集的转录组分析确定了150个差异表达的基因,并丰富了免疫/炎症通路.
结论:
- * 机器学习优化有效地预测了患者在结核病护理中的药剂师干预措施的资格.
- * 仅基于临床变量的临床预测模型为个性化结核病治疗资源分配提供了强大的工具.
- *高预测准确度表明临床实施的潜力,以提高患者管理.
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